Typically, an aircraft fuel system is designed for various fluid flow scenarios that are influenced by diverse design constraints. Thereby, an aircraft fuel system designer considers scenarios ranging from tank refueling, in-flight conditions, landing and the like. Further, by coupling these operating scenarios with trade-offs that occur throughout the design and development process, the number of design decisions can grow exponentially. Therefore, an efficient method for studying and validating the numerous aircraft fuel system designs may be required to ensure quality and safety.
A typical design process for the aircraft fuel system involves studying of one-dimensional (1D) analysis of fuel system schematic networks. Complex network junctions are studied using 1D/three-dimensional (3D) coupling approach, which can be more expensive. Generally, the 1D network uses the experimental or empirical data for individual components appearing in the network. For example, an aircraft fuel system may include a plurality of T-junctions. In existing methods, loss coefficients available for the T-junctions are not sufficient in the domain studying the aircraft fuel system. Literature shows the loss coefficients of different classes (class-1, 2 and 3) in the order of the decreasing accuracy. Further, an automated module provides high quality computational fluid dynamics (CFD) results that can replace the loss coefficients with lesser accuracy (class-2, and class-3 data). Few information is available for the T-junctions in the literature by using 3D CFD codes, however the quantity of loss coefficients is very less and the results are generally, generated using a manual approach (i.e., manual geometry creation, mesh creation and simulation and post processing). Furthermore, thousands of CFD computations are required to encompass all possible configurations.
In addition, the problem in simulating the 1D network include simulation convergence, which can require, either significant time to work-around the problem or to remove the T-Junctions, thereby reducing the accuracy of the simulations. This convergence problem is partly due to the complexity of the mathematical model used in the 1D network and predominantly due to inconsistencies and/or inaccuracy in the loss coefficient surface data (f {Area Ratio, Flow Ratio}) for different flow regimes used in iterative computational methods.
FIG. 1 is a graph 100 illustrating the problematic regions in a T-junction database used in particular type of T-junction simulations of the 1D network. The solid lines illustrate experimental results with different classes (class-1, 2 and 3) in the order of the decreasing accuracy. As shown in region 102, the T-junction database lacks data in some regions, such as low area ratios and different flow regimes (i.e., symmetric combining and dividing flows). If any T-junction present in the fuel network comes across such regions, where the data is not available, it may result in the convergence problems for the network in a Flowmaster. Hence, the Flowmaster may recommend removing the T-junction; however, this may not always be possible as it can contribute to significant pressure losses in the aircraft fuel system.
The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.